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Volume 40 Issue 9
Sep.  2014
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Journal of Beijing University of Aeronautics and Astronautics  > 2014  >  40(9): 1176-1182.
Zheng Hong, Zhou Lei, Yang Haoet al. Rolling element bearing fault diagnosis based on spectral kurtosis and bi-spectrum[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(9): 1176-1182. doi: 10.13700/j.bh.1001-5965.2013.0628(in Chinese)
Citation: Zheng Hong, Zhou Lei, Yang Haoet al. Rolling element bearing fault diagnosis based on spectral kurtosis and bi-spectrum[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(9): 1176-1182. doi: 10.13700/j.bh.1001-5965.2013.0628(in Chinese)
shu

Rolling element bearing fault diagnosis based on spectral kurtosis and bi-spectrum

doi: 10.13700/j.bh.1001-5965.2013.0628
  • 1.

    School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China

  • 2. China Academy of Civil Aviation Science and Technology, Beijing 100028, China

  • Received Date: 04 Nov 2013
  • Publish Date: 20 Sep 2014
    Abstract
  • Aiming to deal with the bearing fault diagnosis problem in the case of strong background noise being contained in the collected vibration signals, a rolling element bearing fault diagnosis method based on spectral kurtosis and bi-spectrum was proposed. Spectral kurtosis was used to indicate the frequency band where the transient occurred and the optimal band-pass filter was adopted to remove the background noise. According to the bandwidth of the band-pass filter, a low-frequency rectangular area was determined, and bi-spectrum of the envelope of the band-pass filtered signal on this rectangular area was computed. The bearing fault was diagnosed according to the bi-spectrum graph. Analysis on simulation signals shows that strong background noise and rolling element slip can give rise to the failure of traditional diagnosis methods (e.g. kurtosis, power spectrum and envelop spectrum); the proposed method can inhibit the noise effectively and diagnose the rolling element bearing fault more accurately. A diagnosis instance of the bearing 6205-2RS JEM SKF was presented to show the effectiveness of the proposed method.

     

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